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What Are The Top 5 Data Tools Startups Must Have


6 min • 20 January, 2021

We’ve all heard that “data is the new oil,” and it’s more than just a cliche, as data impacts our lives in tremendous ways: From the recommendations we get on Netflix, Spotify, Amazon, and Tinder, to the products we see at retailers like Walmart, to even the information we consume on YouTube, Google, Facebook, and more. These massive corporations can afford large teams of in-house data scientists and data engineers to build pipelines and deploy machine learning models that impact literally billions of lives. However, small and medium enterprises can achieve similar results from data and AI, without the huge expenses, using data tools that handle the hard grunt work in the back-end. Let’s take a look at the top 5 popular data tools startups must use to gain a competitive edge.


Tableau is a popular data tool, which is more typically used by enterprises, but can add value to startups as well. While Tableau isn’t as easy to get up and running as some of the other options in this list, Tableau is more tailored towards “social BI,” or social business intelligence. In simpler terms, Tableau is a great tool for business analysts and other Business Intelligence professionals to collaborate on data-driven reports. One thing to keep in mind is that Tableau tends to fall short on the data preparation front, which means you may need to use additional tools like Tableau Prep, adding to its complexity. Business Intelligence is all about distilling technical information — typically regarding the analysis of high volumes of data — for a non-technical audience of business leaders. This is where tools like Tableau come into play, which enables semi-technical people to create high-level reports. For inspiration and ideas on reports, you can take a look at Tableau’s public gallery, which features popular visual reports like the movie-year in review, below. Netflix dataset from Tableau Public


**Looker **is about more than just creating dashboards and visualizations — it’s about having fine-grained control over your data and what you’re sharing, known as the Looker “data model.” Looker aims to strike a balance between governance and self-service, which means that you can fine-tune business rules for accessing and interacting with data. This layer of business logic is the main differentiator between Looker and Tableau, and it’ll come into handy if you have many people working on analytics and reports. However, this is less likely to be useful for startups. What may be more useful is Looker’s strong SQL capabilities, which enables a highly-technical audience to manage data with a high degree of control. That said, if you’re non-technical (or have a limited budget), then Looker may not be for you. Ultimately, Looker is a renowned giant in the Business Intelligence space, and as a BI tool, it’s a solid alternative to Tableau.


**Dataiku **stands out from the platforms we’ve featured so far in that it’s geared towards a technical audience. In other words, it’s not the first step to data science — Dataiku assumes you’re already on that journey and have some technical oomph in your organization. While technical users can gain value from Gyana, Tableau, and Looker, these platforms are designed to be easier-to-use, even among less-technical crowds. You can see on Dataiku’s site that the first audience they’re facing is tech experts. Tech experts This includes professionals like data scientists, data engineers, software engineers, and data architects. If you have a growing startup, or a startup focused on data and AI, then you’re likely to employ some of these roles, making Dataiku a solid option to consider. One great feature about Dataiku is that it fits into a variety of technical workflows. Dataiku will work for you whether you’re using AWS, GCP, Azure, or on-premise solutions; whether you’re using Python, R, Julia, or Scala; whether you’re using Spark and Kubernetes clusters or want to deploy with Dataiku. Ultimately, if you’re highly-technical, Dataiku should be on the list of tools to consider.


**PowerBI **is Microsoft’s answer to Tableau, which provides interactive visualizations and business intelligence capabilities. This powerhouse tool is used by BI professionals at organizations around the world, though it’s more favoured among the enterprise crowd. The sleek design is deceptively complex, and it can take a lot of work to get up and running. Indeed, even the “getting started” **tutorials **are long and complex, so PowerBI won’t suit your needs if you’re looking to do data science effortlessly. In short, PowerBI is a powerful tool to consider if you have the technical bandwidth to handle it.


**Gyana **is a powerful, insightful and easy-to-use no-code data science tool, enabling anyone to become a citizen data scientist in minutes. The traditional road to implementing data science in a business is highly expensive, time-consuming, and complex, typically involving not only data scientists, but data engineers as well. Gyana does the heavy lifting in the background, so you can simply connect data — from a variety of sources — and find insights in clicks. Making predictions, visualizations, and shareable reports has never been easier, thanks to the suite of no-code tools provided by Gyana, making this a must-have for startups. Crucially, Gyana is also ISO 27001 certified, demonstrating that Gyana has invested in the tools and systems needed to protect your data. Data security is a hot-topic in 2021, and you can rest easy using Gyana. Gyana stands out by catering to an audience of business users, which fall under the category of “citizen developers.” Tools like Excel and Google Sheets — which cater to novice users — and tools like Looker, Dataiku, and PowerBI — which cater to engineers — abound. Gyana fills the gap for non-technical business professionals. [caption id="attachment_1491" align="aligncenter" width="624"]Illustration by Charge VC Graphic by Charge VC.[/caption] Take a product tour of Gyana to learn more.


The data and AI revolution continues to change the world, and to become a part of it, take advantage of some of these tools. We’ve really just scratched the surface, as there are hundreds of data tools out there, but these are a good place to start searching.